{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Arterial line study\n",
    "\n",
    "This notebook reproduces the arterial line study in MIMIC-III. The following is an outline of the notebook:\n",
    "\n",
    "1. Generate necessary materialized views in SQL\n",
    "2. Combine materialized views and acquire a single dataframe\n",
    "3. Write this data to file for use in R\n",
    "\n",
    "The R code then evaluates whether an arterial line is associated with mortality after propensity matching.\n",
    "\n",
    "Note that the original arterial line study used a genetic algorithm to select the covariates in the propensity score. We omit the genetic algorithm step, and instead use the final set of covariates described by the authors. For more detail, see:\n",
    "\n",
    "> Hsu DJ, Feng M, Kothari R, Zhou H, Chen KP, Celi LA. The association between indwelling arterial catheters and mortality in hemodynamically stable patients with respiratory failure: a propensity score analysis. CHEST Journal. 2015 Dec 1;148(6):1470-6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import psycopg2\n",
    "import os\n",
    "\n",
    "# below is used to print out pretty pandas dataframes\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "def execute_query_safely(sql, con):\n",
    "    cur = con.cursor()\n",
    "    \n",
    "    # try to execute the query\n",
    "    try:\n",
    "        cur.execute(sql)\n",
    "    except:\n",
    "        # if an exception, rollback, rethrow the exception - finally closes the connection\n",
    "        cur.execute('rollback;')\n",
    "        raise\n",
    "    finally:\n",
    "        cur.close()\n",
    "    \n",
    "    return\n",
    "        \n",
    "\n",
    "# location of the queries to generate aline specific materialized views\n",
    "aline_path = './'\n",
    "\n",
    "# location of the queries to generate materialized views from the MIMIC code repository\n",
    "concepts_path = '../../concepts/'\n",
    "\n",
    "# specify user/password/where the database is\n",
    "sqluser = 'postgres'\n",
    "sqlpass = 'postgres'\n",
    "dbname = 'mimic'\n",
    "schema_name = 'mimiciii'\n",
    "host = 'localhost'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# connect to the database\n",
    "con = psycopg2.connect(dbname=dbname, user=sqluser, password=sqlpass, host=host)\n",
    "\n",
    "# all queries are prepended by this statement to ensure we use the correct schema\n",
    "query_schema = 'SET SEARCH_PATH TO public,' + schema_name + ';'\n",
    "# note that by placing 'public' first, we create materialized views on the public schema\n",
    "# ... but can still access tables on the `schema_name` table (usually mimiciii)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 - Generate materialized views\n",
    "\n",
    "Before generating the aline cohort, we require the following materialized views to be already generated:\n",
    "\n",
    "* angus - from `angus.sql`\n",
    "* weightdurations - from `weight-durations.sql`\n",
    "* heightweight - from `heightweight.sql`\n",
    "* aline_vaso_flag - from `aline_vaso_flag.sql`\n",
    "\n",
    "You can generate the above by executing the below codeblock. If you haven't changed the directory structure, the below should work, otherwise you may need to modify the `concepts_path` variable above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating materialized view using ../../concepts/sepsis/angus.sql ... done.\n",
      "Generating materialized view using ../../concepts/demographics/HeightWeightQuery.sql ... done.\n",
      "Generating materialized view using ./aline_vaso_flag.sql ... done.\n"
     ]
    }
   ],
   "source": [
    "# list of queries we will execute in turn\n",
    "queries = [\n",
    "    'sepsis/angus.sql',\n",
    "    'durations/weight-durations.sql',\n",
    "    'demographics/heightweight.sql',\n",
    "    'aline_vaso_flag.sql'\n",
    "]\n",
    "\n",
    "for query_file in queries:\n",
    "    # load in the text of the query\n",
    "    f = os.path.join(concepts_path, query_file)\n",
    "    with open(f) as fp:\n",
    "        query = ''.join(fp.readlines())\n",
    "\n",
    "    # Execute the query\n",
    "    print('Generating materialized view using {} ...'.format(f),end=' ')\n",
    "    execute_query_safely(query_schema + query, con)\n",
    "    print('done.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we generate the *aline_cohort* table using the aline_cohort.sql file.\n",
    "\n",
    "Afterwards, we can generate the remaining 6 materialized views in any order, as they all depend on only *aline_cohort* and raw MIMIC-III data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating materialized view using ./aline_cohort.sql ... done.\n"
     ]
    }
   ],
   "source": [
    "# Load in the query from file\n",
    "f = os.path.join(aline_path,'aline_cohort.sql')\n",
    "with open(f) as fp:\n",
    "    query = ''.join(fp.readlines())\n",
    "    \n",
    "# Execute the query\n",
    "print('Generating materialized view using {} ...'.format(f),end=' ')\n",
    "execute_query_safely(query_schema + query, con)\n",
    "print('done.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14024 - exclusion_readmission\n",
      " 8948 - exclusion_shortstay\n",
      "17399 - exclusion_vasopressors\n",
      "17007 - exclusion_septic\n",
      "12961 - exclusion_aline_before_admission\n",
      "29452 - exclusion_not_ventilated_first24hr\n",
      "15499 - exclusion_service_surgical\n",
      "Will remove 49908 of 52430 patients.\n",
      "\n",
      "\n",
      "Reproducing the flow of the flowchart from Chest paper.\n",
      "52430 - removing 20816 (39.70%) patients - short stay // readmission.\n",
      "31614 - removing 15738 (49.78%) patients - not ventilated in first 24 hours.\n",
      "15876\n",
      "      - removing  5716 (36.00%) patients - additional  5716 36.00% - exclusion_septic\n",
      "      - removing  9112 (57.39%) patients - additional  5753 36.24% - exclusion_vasopressors\n",
      "      - removing  7603 (47.89%) patients - additional  1598 10.07% - exclusion_aline_before_admission\n",
      "      - removing  6750 (42.52%) patients - additional   287 1.81% - exclusion_service_surgical\n",
      "2522 - final cohort.\n"
     ]
    }
   ],
   "source": [
    "query = query_schema + \"\"\"\n",
    "select\n",
    "icustay_id\n",
    ", exclusion_readmission\n",
    ", exclusion_shortstay\n",
    ", exclusion_vasopressors\n",
    ", exclusion_septic\n",
    ", exclusion_aline_before_admission\n",
    ", exclusion_not_ventilated_first24hr\n",
    ", exclusion_service_surgical\n",
    "from aline_cohort_all\n",
    "\"\"\"\n",
    "\n",
    "# Load the result of the query into a dataframe\n",
    "df = pd.read_sql_query(query, con)\n",
    "\n",
    "# print out exclusions\n",
    "idxRem = df['icustay_id'].isnull()\n",
    "for c in df.columns:\n",
    "    if 'exclusion_' in c:\n",
    "        print('{:5d} - {}'.format(df[c].sum(), c))\n",
    "        idxRem[df[c]==1] = True   \n",
    "        \n",
    "# final exclusion (excl sepsis/something else)\n",
    "print('Will remove {} of {} patients.'.format(np.sum(idxRem), df.shape[0]))\n",
    "\n",
    "\n",
    "print('')\n",
    "print('')\n",
    "print('Reproducing the flow of the flowchart from Chest paper.')\n",
    "\n",
    "# first stay\n",
    "idxRem = (df['exclusion_readmission']==1) | (df['exclusion_shortstay']==1)\n",
    "print('{:5d} - removing {:5d} ({:2.2f}%) patients - short stay // readmission.'.format(\n",
    "        df.shape[0], np.sum(idxRem), 100.0*np.mean(idxRem)))\n",
    "df = df.loc[~idxRem,:]\n",
    "\n",
    "idxRem = df['exclusion_not_ventilated_first24hr']==1\n",
    "print('{:5d} - removing {:5d} ({:2.2f}%) patients - not ventilated in first 24 hours.'.format(\n",
    "        df.shape[0], np.sum(idxRem), 100.0*np.mean(idxRem)))\n",
    "\n",
    "df = df.loc[df['exclusion_not_ventilated_first24hr']==0,:]\n",
    "\n",
    "print('{:5d}'.format(df.shape[0]))\n",
    "idxRem = df['icustay_id'].isnull()\n",
    "for c in ['exclusion_septic', 'exclusion_vasopressors',\n",
    "            'exclusion_aline_before_admission', 'exclusion_service_surgical']:\n",
    "    print('{:5s} - removing {:5d} ({:2.2f}%) patients - additional {:5d} {:2.2f}% - {}'.format(\n",
    "            '', df[c].sum(), 100.0*df[c].mean(),\n",
    "            np.sum((idxRem==0)&(df[c]==1)), 100.0*np.mean((idxRem==0)&(df[c]==1)),\n",
    "            c))\n",
    "    idxRem = idxRem | (df[c]==1)\n",
    "\n",
    "df = df.loc[~idxRem,:]\n",
    "print('{} - final cohort.'.format(df.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following codeblock loads in the SQL from each file in the aline subfolder and executes the query to generate the materialized view. We specifically exclude the aline_cohort.sql file as we have already executed it above. Again, the order of query execution does not matter for these queries. Note also that the filenames are the same as the created materialized view names for convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing aline_bmi.sql ... done.\n",
      "Executing aline_vitals.sql ... done.\n",
      "Executing aline_sedatives.sql ... done.\n",
      "Executing aline_icd.sql ... done.\n",
      "Executing aline_labs.sql ... done.\n",
      "Executing aline_sofa.sql ... done.\n"
     ]
    }
   ],
   "source": [
    "# get a list of all files in the subfolder\n",
    "aline_queries = [f for f in os.listdir(aline_path) \n",
    "                 # only keep the filename if it is actually a file (and not a directory)\n",
    "                if os.path.isfile(os.path.join(aline_path,f))\n",
    "                 # and only keep the filename if it is an SQL file\n",
    "                & f.endswith('.sql')\n",
    "                # and we do *not* want aline_cohort - it's generated above\n",
    "                & (f != 'aline_cohort.sql') & (f != 'aline_vaso_flag.sql')]\n",
    "\n",
    "for f in aline_queries:\n",
    "    print('Executing {} ...'.format(f), end=' ')\n",
    "    \n",
    "    with open(os.path.join(aline_path,f)) as fp:\n",
    "        query = ''.join(fp.readlines())\n",
    "        \n",
    "    execute_query_safely(query_schema + query, con)\n",
    "        \n",
    "    print('done.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Summarize the cohort exclusions before we pull all the data together."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 - Extract all covariates and outcome measures\n",
    "\n",
    "We now aggregate all the data from the various views into a single dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/dist-packages/numpy/lib/function_base.py:4116: RuntimeWarning: Invalid value encountered in percentile\n",
      "  interpolation=interpolation)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>subject_id</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>41220.210547</td>\n",
       "      <td>29718.436646</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>15635.000000</td>\n",
       "      <td>31280.000000</td>\n",
       "      <td>66550.500000</td>\n",
       "      <td>99881.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hadm_id</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>149910.327121</td>\n",
       "      <td>29250.749642</td>\n",
       "      <td>100016.000000</td>\n",
       "      <td>124288.500000</td>\n",
       "      <td>150295.000000</td>\n",
       "      <td>175301.000000</td>\n",
       "      <td>199962.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>icustay_id</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>250851.056701</td>\n",
       "      <td>28929.896901</td>\n",
       "      <td>200019.000000</td>\n",
       "      <td>226867.250000</td>\n",
       "      <td>251784.500000</td>\n",
       "      <td>275922.500000</td>\n",
       "      <td>299995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>64.548664</td>\n",
       "      <td>50.095172</td>\n",
       "      <td>16.203184</td>\n",
       "      <td>42.348493</td>\n",
       "      <td>57.102672</td>\n",
       "      <td>73.750108</td>\n",
       "      <td>300.052602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour_icu_intime</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>12.761301</td>\n",
       "      <td>7.530773</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>icu_hour_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.405234</td>\n",
       "      <td>0.491035</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>icu_los_day</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>3.655772</td>\n",
       "      <td>3.403398</td>\n",
       "      <td>1.000579</td>\n",
       "      <td>1.708484</td>\n",
       "      <td>2.524514</td>\n",
       "      <td>4.337101</td>\n",
       "      <td>37.304780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hospital_los_day</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>8.443135</td>\n",
       "      <td>7.820824</td>\n",
       "      <td>0.038194</td>\n",
       "      <td>3.806424</td>\n",
       "      <td>6.446528</td>\n",
       "      <td>10.523090</td>\n",
       "      <td>123.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hosp_exp_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.131245</td>\n",
       "      <td>0.337735</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>icu_exp_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.090801</td>\n",
       "      <td>0.287383</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mort_day</th>\n",
       "      <td>910.0</td>\n",
       "      <td>429.589134</td>\n",
       "      <td>690.777814</td>\n",
       "      <td>0.000694</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3731.972222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day_28_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.155829</td>\n",
       "      <td>0.362765</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mort_day_censored</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>250.882677</td>\n",
       "      <td>435.993963</td>\n",
       "      <td>0.000694</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>3731.972222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>censor_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.639175</td>\n",
       "      <td>0.480335</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aline_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.514274</td>\n",
       "      <td>0.499895</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aline_time_day</th>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.286710</td>\n",
       "      <td>0.711349</td>\n",
       "      <td>0.000694</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.843750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight_first</th>\n",
       "      <td>2445.0</td>\n",
       "      <td>80.887485</td>\n",
       "      <td>26.926383</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>710.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>height_first</th>\n",
       "      <td>896.0</td>\n",
       "      <td>169.893683</td>\n",
       "      <td>17.069401</td>\n",
       "      <td>15.240000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>444.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bmi</th>\n",
       "      <td>896.0</td>\n",
       "      <td>0.003434</td>\n",
       "      <td>0.014513</td>\n",
       "      <td>0.000815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.434431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sofa_first</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>4.365583</td>\n",
       "      <td>1.997711</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>map_first</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>84.703937</td>\n",
       "      <td>17.379634</td>\n",
       "      <td>-6.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>259.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hr_first</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>87.084457</td>\n",
       "      <td>19.483748</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>85.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>174.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp_first</th>\n",
       "      <td>2513.0</td>\n",
       "      <td>36.807791</td>\n",
       "      <td>0.912327</td>\n",
       "      <td>32.222222</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.444446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spo2_first</th>\n",
       "      <td>2519.0</td>\n",
       "      <td>98.642318</td>\n",
       "      <td>3.020366</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bun_first</th>\n",
       "      <td>2473.0</td>\n",
       "      <td>20.190053</td>\n",
       "      <td>15.138929</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>139.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>creatinine_first</th>\n",
       "      <td>2473.0</td>\n",
       "      <td>1.140356</td>\n",
       "      <td>1.168819</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chloride_first</th>\n",
       "      <td>2480.0</td>\n",
       "      <td>104.075403</td>\n",
       "      <td>5.936225</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hgb_first</th>\n",
       "      <td>2478.0</td>\n",
       "      <td>12.226190</td>\n",
       "      <td>2.236309</td>\n",
       "      <td>4.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>platelet_first</th>\n",
       "      <td>2470.0</td>\n",
       "      <td>240.640081</td>\n",
       "      <td>101.252615</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1313.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>potassium_first</th>\n",
       "      <td>2484.0</td>\n",
       "      <td>4.065419</td>\n",
       "      <td>0.681024</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sodium_first</th>\n",
       "      <td>2480.0</td>\n",
       "      <td>139.501613</td>\n",
       "      <td>4.876117</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>165.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tco2_first</th>\n",
       "      <td>2496.0</td>\n",
       "      <td>25.122196</td>\n",
       "      <td>5.202271</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wbc_first</th>\n",
       "      <td>2469.0</td>\n",
       "      <td>12.229850</td>\n",
       "      <td>6.164498</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chf_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.070975</td>\n",
       "      <td>0.256835</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>afib_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.138382</td>\n",
       "      <td>0.345369</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>renal_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.065028</td>\n",
       "      <td>0.246624</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>liver_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.057891</td>\n",
       "      <td>0.233583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>copd_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.015464</td>\n",
       "      <td>0.123413</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cad_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.093577</td>\n",
       "      <td>0.291296</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stroke_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.158604</td>\n",
       "      <td>0.365379</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>malignancy_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.156225</td>\n",
       "      <td>0.363141</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>respfail_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.365583</td>\n",
       "      <td>0.481689</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>endocarditis_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ards_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.017050</td>\n",
       "      <td>0.129483</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pneumonia_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.178033</td>\n",
       "      <td>0.382617</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sedative_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.211340</td>\n",
       "      <td>0.408340</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>midazolam_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.024980</td>\n",
       "      <td>0.156096</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fentanyl_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.040841</td>\n",
       "      <td>0.197960</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>propofol_flag</th>\n",
       "      <td>2522.0</td>\n",
       "      <td>0.187946</td>\n",
       "      <td>0.390747</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    count           mean           std            min  \\\n",
       "subject_id         2522.0   41220.210547  29718.436646      22.000000   \n",
       "hadm_id            2522.0  149910.327121  29250.749642  100016.000000   \n",
       "icustay_id         2522.0  250851.056701  28929.896901  200019.000000   \n",
       "age                2522.0      64.548664     50.095172      16.203184   \n",
       "hour_icu_intime    2522.0      12.761301      7.530773       0.000000   \n",
       "icu_hour_flag      2522.0       0.405234      0.491035       0.000000   \n",
       "icu_los_day        2522.0       3.655772      3.403398       1.000579   \n",
       "hospital_los_day   2522.0       8.443135      7.820824       0.038194   \n",
       "hosp_exp_flag      2522.0       0.131245      0.337735       0.000000   \n",
       "icu_exp_flag       2522.0       0.090801      0.287383       0.000000   \n",
       "mort_day            910.0     429.589134    690.777814       0.000694   \n",
       "day_28_flag        2522.0       0.155829      0.362765       0.000000   \n",
       "mort_day_censored  2522.0     250.882677    435.993963       0.000694   \n",
       "censor_flag        2522.0       0.639175      0.480335       0.000000   \n",
       "aline_flag         2522.0       0.514274      0.499895       0.000000   \n",
       "aline_time_day     1297.0       0.286710      0.711349       0.000694   \n",
       "weight_first       2445.0      80.887485     26.926383       1.000000   \n",
       "height_first        896.0     169.893683     17.069401      15.240000   \n",
       "bmi                 896.0       0.003434      0.014513       0.000815   \n",
       "sofa_first         2522.0       4.365583      1.997711       0.000000   \n",
       "map_first          2522.0      84.703937     17.379634      -6.000000   \n",
       "hr_first           2522.0      87.084457     19.483748       0.000000   \n",
       "temp_first         2513.0      36.807791      0.912327      32.222222   \n",
       "spo2_first         2519.0      98.642318      3.020366      47.000000   \n",
       "bun_first          2473.0      20.190053     15.138929       1.000000   \n",
       "creatinine_first   2473.0       1.140356      1.168819       0.100000   \n",
       "chloride_first     2480.0     104.075403      5.936225      56.000000   \n",
       "hgb_first          2478.0      12.226190      2.236309       4.100000   \n",
       "platelet_first     2470.0     240.640081    101.252615       6.000000   \n",
       "potassium_first    2484.0       4.065419      0.681024       1.800000   \n",
       "sodium_first       2480.0     139.501613      4.876117      74.000000   \n",
       "tco2_first         2496.0      25.122196      5.202271       3.000000   \n",
       "wbc_first          2469.0      12.229850      6.164498       0.200000   \n",
       "chf_flag           2522.0       0.070975      0.256835       0.000000   \n",
       "afib_flag          2522.0       0.138382      0.345369       0.000000   \n",
       "renal_flag         2522.0       0.065028      0.246624       0.000000   \n",
       "liver_flag         2522.0       0.057891      0.233583       0.000000   \n",
       "copd_flag          2522.0       0.015464      0.123413       0.000000   \n",
       "cad_flag           2522.0       0.093577      0.291296       0.000000   \n",
       "stroke_flag        2522.0       0.158604      0.365379       0.000000   \n",
       "malignancy_flag    2522.0       0.156225      0.363141       0.000000   \n",
       "respfail_flag      2522.0       0.365583      0.481689       0.000000   \n",
       "endocarditis_flag  2522.0       0.000000      0.000000       0.000000   \n",
       "ards_flag          2522.0       0.017050      0.129483       0.000000   \n",
       "pneumonia_flag     2522.0       0.178033      0.382617       0.000000   \n",
       "sedative_flag      2522.0       0.211340      0.408340       0.000000   \n",
       "midazolam_flag     2522.0       0.024980      0.156096       0.000000   \n",
       "fentanyl_flag      2522.0       0.040841      0.197960       0.000000   \n",
       "propofol_flag      2522.0       0.187946      0.390747       0.000000   \n",
       "\n",
       "                             25%            50%            75%            max  \n",
       "subject_id          15635.000000   31280.000000   66550.500000   99881.000000  \n",
       "hadm_id            124288.500000  150295.000000  175301.000000  199962.000000  \n",
       "icustay_id         226867.250000  251784.500000  275922.500000  299995.000000  \n",
       "age                    42.348493      57.102672      73.750108     300.052602  \n",
       "hour_icu_intime         5.000000      14.000000      19.000000      23.000000  \n",
       "icu_hour_flag           0.000000       0.000000       1.000000       1.000000  \n",
       "icu_los_day             1.708484       2.524514       4.337101      37.304780  \n",
       "hospital_los_day        3.806424       6.446528      10.523090     123.687500  \n",
       "hosp_exp_flag           0.000000       0.000000       0.000000       1.000000  \n",
       "icu_exp_flag            0.000000       0.000000       0.000000       1.000000  \n",
       "mort_day                     NaN            NaN            NaN    3731.972222  \n",
       "day_28_flag             0.000000       0.000000       0.000000       1.000000  \n",
       "mort_day_censored     150.000000     150.000000     150.000000    3731.972222  \n",
       "censor_flag             0.000000       1.000000       1.000000       1.000000  \n",
       "aline_flag              0.000000       1.000000       1.000000       1.000000  \n",
       "aline_time_day               NaN            NaN            NaN      11.843750  \n",
       "weight_first                 NaN            NaN            NaN     710.000000  \n",
       "height_first                 NaN            NaN            NaN     444.500000  \n",
       "bmi                          NaN            NaN            NaN       0.434431  \n",
       "sofa_first              3.000000       4.000000       5.000000      15.000000  \n",
       "map_first              73.000000      84.000000      95.000000     259.000000  \n",
       "hr_first               74.000000      85.000000      99.000000     174.000000  \n",
       "temp_first                   NaN            NaN            NaN      40.444446  \n",
       "spo2_first                   NaN            NaN            NaN     100.000000  \n",
       "bun_first                    NaN            NaN            NaN     139.000000  \n",
       "creatinine_first             NaN            NaN            NaN      18.800000  \n",
       "chloride_first               NaN            NaN            NaN     129.000000  \n",
       "hgb_first                    NaN            NaN            NaN      19.800000  \n",
       "platelet_first               NaN            NaN            NaN    1313.000000  \n",
       "potassium_first              NaN            NaN            NaN       9.200000  \n",
       "sodium_first                 NaN            NaN            NaN     165.000000  \n",
       "tco2_first                   NaN            NaN            NaN      53.000000  \n",
       "wbc_first                    NaN            NaN            NaN      94.000000  \n",
       "chf_flag                0.000000       0.000000       0.000000       1.000000  \n",
       "afib_flag               0.000000       0.000000       0.000000       1.000000  \n",
       "renal_flag              0.000000       0.000000       0.000000       1.000000  \n",
       "liver_flag              0.000000       0.000000       0.000000       1.000000  \n",
       "copd_flag               0.000000       0.000000       0.000000       1.000000  \n",
       "cad_flag                0.000000       0.000000       0.000000       1.000000  \n",
       "stroke_flag             0.000000       0.000000       0.000000       1.000000  \n",
       "malignancy_flag         0.000000       0.000000       0.000000       1.000000  \n",
       "respfail_flag           0.000000       0.000000       1.000000       1.000000  \n",
       "endocarditis_flag       0.000000       0.000000       0.000000       0.000000  \n",
       "ards_flag               0.000000       0.000000       0.000000       1.000000  \n",
       "pneumonia_flag          0.000000       0.000000       0.000000       1.000000  \n",
       "sedative_flag           0.000000       0.000000       0.000000       1.000000  \n",
       "midazolam_flag          0.000000       0.000000       0.000000       1.000000  \n",
       "fentanyl_flag           0.000000       0.000000       0.000000       1.000000  \n",
       "propofol_flag           0.000000       0.000000       0.000000       1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load in the query from file\n",
    "query = query_schema + \"\"\"\n",
    "--FINAL QUERY\n",
    "select\n",
    "  co.subject_id, co.hadm_id, co.icustay_id\n",
    "\n",
    "  -- static variables from patient tracking tables\n",
    "  , co.age\n",
    "  , co.gender\n",
    "  -- , co.gender_num -- gender, 0=F, 1=M\n",
    "  , co.intime as icustay_intime\n",
    "  , co.day_icu_intime -- day of week, text\n",
    "  --, co.day_icu_intime_num -- day of week, numeric (0=Sun, 6=Sat)\n",
    "  , co.hour_icu_intime -- hour of ICU admission (24 hour clock)\n",
    "  , case \n",
    "      when co.hour_icu_intime >= 7\n",
    "       and co.hour_icu_intime < 19\n",
    "         then 1\n",
    "      else 0\n",
    "    end as icu_hour_flag\n",
    "  , co.outtime as icustay_outtime\n",
    "\n",
    "  -- outcome variables\n",
    "  , co.icu_los_day\n",
    "  , co.hospital_los_day\n",
    "  , co.hosp_exp_flag -- 1/0 patient died within current hospital stay\n",
    "  , co.icu_exp_flag -- 1/0 patient died within current ICU stay\n",
    "  , co.mort_day -- days from ICU admission to mortality, if they died\n",
    "  , co.day_28_flag -- 1/0 whether the patient died 28 days after *ICU* admission\n",
    "  , co.mort_day_censored -- days until patient died *or* 150 days (150 days is our censor time)\n",
    "  , co.censor_flag -- 1/0 did this patient have 150 imputed in mort_day_censored\n",
    "\n",
    "  -- aline flags\n",
    "  -- , co.initial_aline_flag -- always 0, we remove patients admitted w/ aline\n",
    "  , co.aline_flag -- 1/0 did the patient receive an aline\n",
    "  , co.aline_time_day -- if the patient received aline, fractional days until aline put in\n",
    "\n",
    "  -- demographics extracted using regex + echos\n",
    "  , bmi.weight as weight_first\n",
    "  , bmi.height as height_first\n",
    "  , bmi.bmi\n",
    "\n",
    "  -- service patient was admitted to the ICU under\n",
    "  , co.service_unit\n",
    "\n",
    "  -- severity of illness just before ventilation\n",
    "  , so.sofa as sofa_first\n",
    "\n",
    "  -- vital sign value just preceeding ventilation\n",
    "  , vi.map as map_first\n",
    "  , vi.heartrate as hr_first\n",
    "  , vi.temperature as temp_first\n",
    "  , vi.spo2 as spo2_first\n",
    "\n",
    "  -- labs!\n",
    "  , labs.bun_first\n",
    "  , labs.creatinine_first\n",
    "  , labs.chloride_first\n",
    "  , labs.hgb_first\n",
    "  , labs.platelet_first\n",
    "  , labs.potassium_first\n",
    "  , labs.sodium_first\n",
    "  , labs.tco2_first\n",
    "  , labs.wbc_first\n",
    "\n",
    "  -- comorbidities extracted using ICD-9 codes\n",
    "  , icd.chf as chf_flag\n",
    "  , icd.afib as afib_flag\n",
    "  , icd.renal as renal_flag\n",
    "  , icd.liver as liver_flag\n",
    "  , icd.copd as copd_flag\n",
    "  , icd.cad as cad_flag\n",
    "  , icd.stroke as stroke_flag\n",
    "  , icd.malignancy as malignancy_flag\n",
    "  , icd.respfail as respfail_flag\n",
    "  , icd.endocarditis as endocarditis_flag\n",
    "  , icd.ards as ards_flag\n",
    "  , icd.pneumonia as pneumonia_flag\n",
    "\n",
    "  -- sedative use\n",
    "  , sed.sedative_flag\n",
    "  , sed.midazolam_flag\n",
    "  , sed.fentanyl_flag\n",
    "  , sed.propofol_flag\n",
    "  \n",
    "from aline_cohort co\n",
    "-- The following tables are generated by code within this repository\n",
    "left join aline_sofa so\n",
    "on co.icustay_id = so.icustay_id\n",
    "left join aline_bmi bmi\n",
    "  on co.icustay_id = bmi.icustay_id\n",
    "left join aline_icd icd\n",
    "  on co.hadm_id = icd.hadm_id\n",
    "left join aline_vitals vi\n",
    "  on co.icustay_id = vi.icustay_id\n",
    "left join aline_labs labs\n",
    "  on co.icustay_id = labs.icustay_id\n",
    "left join aline_sedatives sed\n",
    "  on co.icustay_id = sed.icustay_id\n",
    "order by co.icustay_id\n",
    "\"\"\"\n",
    "\n",
    "# Load the result of the query into a dataframe\n",
    "df = pd.read_sql_query(query, con)\n",
    "df.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we need to remove obvious outliers, including correcting ages > 200 to 91.4 (i.e. replace anonymized ages with 91.4, the median age of patients older than 89)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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JkqR5s3R0FaiqW4BbppRd0rf/LXqJ9KC2lwGXjROzle9kwNKVqrqL3hKZqeV/BPzRkL5P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\nkiR1mAm7JEmS1GEm7JIkSVKH/f+ur47zE9Oi2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42265f2e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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bfvTPzP7tfCpw9VD41yFWVZ+pqu+rqrVVtZZBeHtWVf0t7s9LyZ8xuNiTJE9n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\nyiVJkqTODOWSJElSZ/8fNG9509xSF4QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42265a83d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Vn65hz39wRq1xY2gRzbaPq+oP6ulvmf00w73wYdgv26rq8aq6H5gGTm+P6aq6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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4226138d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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V91TVr1fV9x6k7Uur6oqq2ltVT1bV/VX1wao6pareUFVdVXsP0v7VVXVNVX2+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\n/wcwCUjPdGZ6NgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4225fcd210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cr0nyx0kONSq7EV6U2S8IJyV5fVV9T1U9MJndD7iqttds+fDHLZ0wrUr32mn3\nyqr6hqr6nOnnyUlel8290Emm+zX//bT7fRtZy0rTyP+lma0IeFFV/X5VLa3CuLSM+faq+qUsW+Ib\nOD4IzMCm0d2vTPKL0+6lST5QVXdkdquwH5nar+ju31nl9CuS3JLZyOVrk9xVVXdl9k/tD0/yo4us\n/Uh09yeSPDWzW589IrMR549U1QczWxjjhsxGxB+w4tRnJ7k9s3nef5bZnSo+ltl9jz+UT4/Ab2b/\nfXp8flXdVVX7pp8f29CqkkwLmzwryT2Z/UvFX1XVx6vq9sx+GbkhyX+KueJw3BGYgU2lu382yRMz\nC70fzGxZ49uT7Mps1cBnH+K8OzO7l+7VmY1ifs503q9nds/m/Qsv/gh0961JtmcW5P9PZuH3wZnd\nDu36zALzX64455Ykj8tsxcMDmU0f2J/ZLxlfn8+c8rBZXZ7kp5K8PbPpJV8y/WyKKRrd/dtJviyz\nRVn2ZHbnlC/I7L30xsyWA/+yjaoP2Bg1+1dQAABgNUaYAQBgQGAGAIABgRkAAAYsjQ0wUFXPSPJr\nR3jaY6cv9W2oo7l2gM1EYAYYe0CSRx3hOScsopD74GiuHWDTcJcMAAAYMIcZAAAGBGYAABgQmAEA\nYEBgBgCAAYEZAAAGBGYAABj4/7KGlITETeRTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42261387d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42223ae890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xfWp6f/yw1YPdd3ihqpYLq8ve/3gd3T697xy0OW16v6OXXLnd3Xd3969394Xd\n/cTMRpv/j8wugHxOkpevQ21HmjKRo+z7+5nVfWeS87v7D7t76e3tvuEruz3IVZnd5ePcqnpcVX1r\nkm/P7BZ1XzGnGmC9CMzAiejd0/tzV9Dns3PLpy3T5qmrK2dF3ju9//eDNt+zpO2yuvtj3f0TSX5z\n2vSMdajtuwbTO5b7/MPH9C+7+wvLtDlnme1Jku7+RJLrMru39A/my6PLv9fdnxz1BVgLgRk4ER0e\nbXx2VZ27SIfp7goHp9Xzl+6fnjL3P69LdWOH74DxnKp6yhHqeFK+fCeNt8xtP2lp2yXunt5PHrYa\n++3MRnhPzWzax9LaTsnyI9ifm953H2kEf7o13OiXhMP+7fT+w0n+8bTsYj9gQwnMwIno96ZXJXlb\nVf0vVfWo5G8fl31mVf18VT1/Sb/DAfSnpovktk99viOzebdHC6Xr4TeTfGBa/p2qOufwaG5V/cPM\n7vzxkCQ3Zvawj8OeW1V/WlU/Mt0vOVOfh1XVj+TL4fLa1RbW3R/Pl+cR/3JV/eA0NzlV9eQk/zHL\nz73+48yePPiYJG+qqsdN/b62qn44s4e13LFAGXuTfDqzJ//tmJb/w+q+EcBiBGbghDPN631JZrc4\ne1iSf53kjqq6I7PQdmNmF5g9aknXyzO7UO5RmV0kd1dV3ZXkTzO7eO5Hj0Ht9yb5n5J8PLN5zNdN\ndfzXzEL7zszu/vF93X3Pku7fkeTKJAer6gtVdWdmD/O4MrOwv29aXot/kdkDUh6W2WO2/6aqPptZ\nyH9Skn+2zPf6bJIfn1ZfmOSvpn6fz2yE+ECSnznaH97dX8qD5yv/Wnfft1x7gPUgMAMnpCmgfU9m\nj0x+e2YXm31dZqOYv5/kn2c2Wjnf5zNJ/kFmofKvMvs38o4k/yaz26AdyjHQ3Qcye9T0ZUnmH17y\noSQ/m+Rbu/svl3R7R5J/klmI/WBmvxgc/r7XZTbn9x+tNVxOU1eemeTSJIdr+GJmI+NnZ/bLxXJ9\n/3VmDz85PNq8PbMnA746s+P+NwuW8dtzyx6FDWw4j8YG4LhSVT+Z5F8m+bPu/o6jtQdYKyPMABw3\npvtIH774cq3TSwAWIjADcFyoqq/JbCrIrszutf0bm1oQ8FVj+2YXAAAj011Krk5ySpKvnzb/RHff\nvXwvgPUjMAOsUlX9gzz4ArRFfF93/8lG1LOoqnpRkl9cYbendvetG1HPAh6a2VMbv5TZRYKv624X\n+wHHjMCu2HaJAAAATElEQVQMsHon5eiPcz5Sn832tVl53ds2opBFdPe7MrunNsCmcJcMAAAYcNEf\nAAAMCMwAADAgMAMAwIDADAAAAwIzAAAMCMwAADDw/wOoe4anlw0kkAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4222705710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CMwAADAjMAAAwsG2rC+DAsuO8K/aq/U0XPnuDKgEA2BzOMAMAwIDADAAAAwIzAAAMCMwA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42223aef50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42224b2610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42224c5890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fBZ4IvBB4G/A84OeSXAi8BTgAfB14CvBa4AeSbKyqzw1vMMnZwO/z6MmLBxhc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42221ad710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f422267fe90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+MdOpIrPfomzGfreesVj4vDXvW8DWc0Qa2GzHrrP9vK/mj2T/NFOIXriI76TW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\nQJAGAIAB/x+nuhQe+m2SawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42261cba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f422268acd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4226515d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1HJaZ+YpufQHbXjxurfXag6hnD85vc6Hjx9uMfvFzbUbHAPgkgjHA3tytU7+d\n9XkHUcghu0unvh/uchCF7MV2OLqz7Y6DwFnKqBQAAJCL7wAAoBKMAQCgEowBAKASjAEAoBKMAQCg\nEowBAKCq/x/JbVuFzLXAWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4222289c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42264fb710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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X5zeBFcBFwOn05jRP18fpwF/Suyr8MPA84D8AFwMnAWf0lT9K76bAEXoXT3bx\n7JsDHxp27H2e5pkbDV/SfT/QtdONaVgB/gJY0+3/yLM2Jh8A3t3X9Ai9435Z9/lJYGPfz/36Hsa0\nr+OSpKF5ZVrSweCP6AXJH6mq5wMvoBfgvgGMAu/qL07yamADvQsOHwCWd/sdBvwIMA68AvjYsANI\nclTX56HA54FXVNUR3VjOBU4EfmWIrjbSC+QrqupI4PBu/AWsSfIzk4VV9btV9X3AvV3TW6rq+/o+\nbxl2/H193ju5f1/zq/v6fMc+dPcWYDXwNuDwqnohvTB8d3c1e31X97+Akao6vKoOA14MnAnc2Deu\nd+xlTPs6LkkammFa0sHgCeCnquqzAFW1u6rGgPd3288cqL+M3hXfC6vqf1bVjm6/p7s+fhq4Dzg9\nyaohx/DfgaXA/cBPV9XtXZ9PVdXHgQvoPb5uOluAtZOPuKuqb1XVJTwTLAePZT57AfCrVfWhqnoM\noKrur6pvAifT+zPqy1X17qp6YHKnqtpZVX9eVRfMzbAl6RmGaUkHgyuq6sEp2v+y+14xOQc6yb8H\nfpTeVesrp+qsqh4CPtGtnjbkGCavAl8x1fOPq+o64O4h+rmkqqZ6ecvksZw45HjmgweBq/aw7Zvd\n9xFJ/t0sjUeS9plzpiUdDLbsoX1H3/KRwLfoTeOA3lXT7Un21OcLuu+jp/vhSZYAx3ern9lL6WeA\nH5imu+mO5YXTjWceGZ/qkXqdm+nN6X4p8NkklwOfrKp7Zm10kjQEw7Skg8EjUzVW1eN9Yfl53ffk\n0z0W88zNbHszzFXTF/LMvwTet5e6r03XUVVNeSz0XhQDzxzHQrBzTxuqaleSX6T3hsUfBP4EIMm/\nAn9L79nW/zAro5SkvXCahyQ92+Tvi1+qqgzx+S9zOdgF7um9bayqTfSedrIOuI7eXza+DzgP+HSS\nK/b7CCVpGoZpSXq2yce+TTt9Yx/sAr7dLe/xudbTbDsoVdXDVfXhqjq7qpbRe+vih7vNb03yxjkc\nniQZpiVpwGe77xclec1MdFhVTwB3dKun7KX0dTPx86YwGeT3OAF8oaiqO6pqHfC5runHB0u67wV/\nrJIWBsO0JPWpqi/zTFD7nSR7nIOc5LDu5sJh3NB9vzXJEVP09fNMf/Nhq8knYwzz6L15YfBtjlP4\nt+578Nd/wR2rpIXNMC1J3+1X6T2b+seAv09ySpLvAUiyKMkrklxE71F2w07NmHxxzEuATyQ5oetv\ncZK1wEeXw6XBAAACHElEQVTpPY5vf9jafZ+T5NC9Vs4f/y3J5iS/0P/K9yRHJnk38BNd0+aB/SaP\n9bwki2ZhnJIOcoZpSRpQVVuAn6P3CurXAf8EPJbkAXpXRP8FeB+9m+GmeubzVH3uBM6hF9J/GLg9\nyTfovfL7z7o+/7grf2LGDqZn8nnZZwEPJ7k3ybYkG2b458yk0HvF+p8CX0vyaJJd9P5C8oFu+xXd\nTYr9PtJ9vxN4NMlXu2P93dkauKSDi2FakqZQVZ8AXkbvLYlfoBdwj6Q3jeD/ApcAJ1XVV/ehz83A\nKuB6ei8sWQLcA/w2cCq915XDDF+hrqqb6P3l4B/o/WVgGXAsvb8MzFcfB95K7/XpdwJP0Xu2933A\nGPDmqvrlwZ2q6qPdfp8HdtO7kfRY4KjZGbakg02mfpGWJGm2Jfknejco/lJVXT3Hw5EkDcEwLUnz\nQJIfpnfF+9vAaFXdO8dDkiQNwTcgStIsSbKO3nSDjcC2qno6yQuAtwCXdWXXGaQlaeHwyrQkzZIk\n7wf+R7f6NL0bHI/kmftXbgVOq6oH5mB4kqQGXpmWpNmzgd5Nhj8OLAdeRO+Gxjvo3ZT4x1X1b3ve\nfeYl2cK+ve1xY1W9Y3+NR5IWGsO0JM2Sqrod+PW5HseAEXrPvh7Wd71wRpIOZk7zkCRJkhr5nGlJ\nkiSpkWFakiRJamSYliRJkhoZpiVJkqRGhmlJkiSpkWFakiRJavT/AXbN9+3eOEQRAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f422263ad50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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peEyvlz3ulz+7lZN2K5IfSHLphu1LFvv+v+4+nOTwLr3/pqrqaHev7dX789hjzXA6rBdO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\ngEEkAwDAIJIBAGD4f0uRH2khLr+SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42261217d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4226102c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jySVJXlBVZybZkeQJSR6X5F1V9W3dff+03/d29+cP4fkAAMC6WaRn+uwke7r7E919b5LL\nk2xfUmd7kjdPy1cleVZV1VR+eXd/pbs/mWTPdDwAADjsLRKmT05y29z6vqls2TrdfV+Su5KcsMq+\nneRPqurGqrrw4JsOAAAba9VhHmvoe7r79qp6TJJ3VtX/7e4/XVppCtoXJsmpp5663m0EAIAVLdIz\nfXuSx8+tnzKVLVunqrYkOS7JnQfat7v3f38uydVZYfhHd1/a3Wd191lbt25doLkAALA+FgnTNyQ5\no6pOq6qjM3ugcNeSOruSXDAtPy/Je7q7p/Id02wfpyU5I8mfV9Ujq+pRSVJVj0zy7CQfffCnAwAA\n62fVYR7dfV9VXZTkuiRHJbmsu2+uqlcm2d3du5K8Mclbq2pPki9kFrgz1bsyyS1J7kvyku6+v6pO\nSnL17BnFbEnytu5+xxqcHwAArJmFxkx397VJrl1S9vK55XuSPH+FfX89ya8vKftEku842MYCAMBm\n4g2IAAAwSJgGAIBBGzk1HsARZ9vOaw6q/t6Lz1ujlgCwHvRMAwDAIGEaAAAGCdMAADBImAYAgEHC\nNAAADBKmAQBgkKnxADaQqfQADm96pgEAYJAwDQAAg4RpAAAYZMw0DDrYsa4AwJFHzzQAAAwSpgEA\nYJAwDQAAg4RpAAAYJEwDAMAgYRoAAAYJ0wAAMEiYBgCAQcI0AAAMEqYBAGCQMA0AAIOEaQAAGCRM\nAwDAIGEaAAAGCdMAADBImAYAgEHCNAAADBKmAQBgkDANAACDhGkAABi0ZaMbAJvFtp3XbHQTYFUH\n+9/p3ovPW6OWAJDomQYAgGHCNAAADBKmAQBgkDHTAEc446wB1o6eaQAAGCRMAwDAIGEaAAAGCdMA\nADBImAYAgEHCNAAADDI1HkcsrwcHANaanmkAABgkTAMAwCDDPAD4Ot6YCLA4PdMAADBooTBdVedW\n1a1Vtaeqdi6z/ZiqumLafn1VbZvb9itT+a1V9ZxFjwkAAJvdqsM8quqoJK9Pck6SfUluqKpd3X3L\nXLUXJ/lid59eVTuSXJLkBVV1ZpIdSZ6Q5HFJ3lVV3zbts9oxATgCGUYCHEkWGTN9dpI93f2JJKmq\ny5NsTzIffLcnecW0fFWS11VVTeWXd/dXknyyqvZMx8sCxzxsHQl/Uaz1OYxMW7cZ/5yAtZ+G8kj4\nnQocuRYJ0ycnuW1ufV+Sf7pSne6+r6ruSnLCVP7BJfuePC2vdsxNY7P9RZFsvr8s1mNOZ/NGA2tl\ns3UgbLbf8XCoHIn3wqafzaOqLkxy4bT65aq6dSPbc5BOTPL5tThwXbIWR+UA1uxasu4eUtfyCP5d\nseJ1XI9zXuufcQRft+U8pO7JI9whv5YbeC98y6IVFwnTtyd5/Nz6KVPZcnX2VdWWJMcluXOVfVc7\nZpKkuy9NcukC7dx0qmp3d5+10e3gwXMtjxyu5ZHBdTxyuJZHjofqtVxkNo8bkpxRVadV1dGZPVC4\na0mdXUkumJafl+Q93d1T+Y5pto/TkpyR5M8XPCYAAGxqq/ZMT2OgL0pyXZKjklzW3TdX1SuT7O7u\nXUnemOSt0wOGX8gsHGeqd2VmDxbel+Ql3X1/kix3zEN/egAAsHZq1oHMWqiqC6dhKhzmXMsjh2t5\nZHAdjxyu5ZHjoXothWkAABjkdeIAADBImF4DXpV+eKuqvVX1kaq6qap2T2WPrqp3VtXHp+9v2uh2\n8kBVdVlVfa6qPjpXtuy1q5nXTvfph6vqKRvXcpZa4Vq+oqpun+7Nm6rq++e2/cp0LW+tqudsTKtZ\nTlU9vqreW1W3VNXNVfXvpnL35mHkANfxIX9fCtOH2Nzr15+b5MwkPza9Vp3Dy/d295PnpvjZmeTd\n3X1GkndP62w+b0py7pKyla7dczObYeiMzOayf8M6tZHFvCkPvJZJ8prp3nxyd1+bJNPv2B1JnjDt\n81+n38VsDvcl+cXuPjPJ05K8ZLpm7s3Dy0rXMXmI35fC9KH31devd/e9Sfa/Kp3D2/Ykb56W35zk\n/A1sCyvo7j/NbEaheStdu+1J3tIzH0xyfFU9dn1aympWuJYr2Z7k8u7+Snd/MsmezH4Xswl092e6\n+0PT8peSfCyztyG7Nw8jB7iOK3nI3JfC9KG33OvXD/QfG5tPJ/mTqrpxegNnkpzU3Z+Zlv8qyUkb\n0zQGrHTt3KuHp4umf/q/bG64lWt5mKiqbUm+M8n1cW8etpZcx+Qhfl8K0/BA39PdT8nsnxpfUlXP\nmN84vZDINDiHIdfusPeGJP8oyZOTfCbJb25sczgYVfWNSf4oyUu7++75be7Nw8cy1/Ehf18K04fe\nIq9fZxPr7tun788luTqzf5b67P5/Zpy+P7dxLeQgrXTt3KuHme7+bHff393/kOR387V/MnYtN7mq\nenhmAewPuvu/T8XuzcPMctfRfSlMrwWvSj+MVdUjq+pR+5eTPDvJRzO7hhdM1S5I8vaNaSEDVrp2\nu5K8aJo54GlJ7pr7J2c2oSXjZv9FZvdmMruWO6rqmKo6LbMH1/58vdvH8qqqMntT8se6+7fmNrk3\nDyMrXUf35QKvE+fgrPT69Q1uFos7KcnVs98Z2ZLkbd39jqq6IcmVVfXiJJ9K8qMb2EZWUFV/mOSZ\nSU6sqn1Jfi3JxVn+2l2b5Pszeyjmb5P85Lo3mBWtcC2fWVVPzmw4wN4kP5Mk3X1zVV2Z5JbMZhx4\nSXffvxHtZlnfneTHk3ykqm6ayn417s3DzUrX8cce6velNyACAMAgwzwAAGCQMA0AAIOEaQAAGCRM\nAwDAIGEaAAAGCdMAfNU0t+9FVXVTVf1tVfX02TZ9uqpMAwUwMc80APN+NcmrpuV7knx2Wr4/s7nz\n11VVHZ/kpUnS3a9Y758PsBrzTAPwVVX1uSRbk/xCkt/uub8kqurkJO9Oku7+9nVqz7Ykn5x+Zq3H\nzwQ4GMI0AEmSqnpMvtYT/aju/vJGticRpoHNz5hpAPZ7xP6FzRCkAQ4HwjTApKr2Tg/YPbOqHltV\nv1NVt1XV31XVx6rq56vqYXP1n19V/6uq/rqq7q6qa6rqicsc95ip7luq6v9U1eer6p6q+lRV/UFV\nPXXBNp1aVb83temeqvpkVb26qo57kOf9zOmhwr1zZT33ecVUtuIDiFX1pv11p/P9D1X14ar60lR+\n/FTvYVX1E1X13qq6s6r+vqruqKqbq+qyqjp37pjvy9QrvUybvtougI3kAUSABzotyR8m+eYkdyd5\neJJvT/JbSb41yc9V1cVJfjmzB/P+Nsmjknx/ku+qqrO7++NzxzsnyZXTcif56+n71CT/MsmPVtVP\ndfdbD9Cm06djbE3y5Wn/bUl+Mcn2qnpGd39m8HzvzWx4x1FJTpzKPju3/WB6qb8hyZ8mOTvJ32f2\nZzPvrZmd8353JTl2+rlnTp93TNu+kOTzK7TpYNsFsCb0TAM80Gsy6xH9ju4+LrOw97Jp20uq6lcz\ne0DvpUmO6+5jkzwpya1Jjk/y60uO9+Ukr03yjCTf2N2P7u5HJPmWJL+dWcfGpVV16gHa9OrMgufT\nu/tRSR6Z5PzMwubpSd48erLd/Wfd/c1J/slc2TfPfV59EId7SZJvS7Ijs3M9PrPQ/zdV9YzMgvT9\nSX4+ybHT9m9I8rgkP5Hk/XNt+OEDtOlg2wWwJjyACDCpqr2ZBdwvJvnW7v7rJdvfneT7ptVf6+5X\nLtn+9Mx6Zb+SWVC8d8Gf+8YkP5XkFd39n1Zo0z1JntTde5Zs/94k75lWn97d78+g1R72O9D2qnpT\nkgum1ed0958ss/+/T3JJknd093MPRZsANpqeaYAH+p2lQXryrun73syGfCz1gcxC7zGZ9RYv6n9M\n3999gDpXLg3SSdLd703yZ9Pq8w7iZ66VDy8XpCd3T9+PmR97DnA488sM4IE+skL556bvvcvNdtHd\n/5DZsIsk+ab5bVX16Kp6WVX92fTg3X1zD/NdPVV73AHa9L4DbPuf0/dTDlBnvfzvA2x7d2b/I/KU\nJO+rqn9VVQc6Z4BNzwOIAA+00oN896+yfb7Ow/cXVNWZmQ3FOGmu3peS/F1mDxIenVn4fuQBjnv7\nAtu2HqDOerljpQ3d/fGq+jdJXpfk6dNn/1CWdyS5tLv/Yj0aCXCo6JkGWHu/n1mQ/lCSczN7Icqx\n3X3S9ODf86d6R8KY4PsPtLG7L8tstpSXJnl7kjsze0DxXye5cXq4E+CwIUwDrKFpho6zMwuZP9Td\n1y0zROSkB+75AAcaDrF/24q9wptJd3+2u/9Ld5+fWW/62ZkNdakk/7mq/vGGNhDgIAjTAGvrlOn7\nju5eaajGP1/gOP9sgW0fWrhVm0TP3JBZ7/y+zP5e+p65Kv+wf6GqjoSee+AII0wDrK27pu+Tquox\nSzdW1ZPy9S8xWckLqupbl9n/GfnaLCD/bbiV66Cqjl5pW3ffn9lLXpLZbCj73T23fPxatAvgwRCm\nAdbWxzLrca0kV1TV6UlSVQ+vqh9O8s4s9ia/e5P8cVV917T/w6rqB5NcNW1/Z3d/4JC3/tD6jaq6\nqqrOr6pH7y+sqpOq6rWZjaXuzP5MkiTTFIWfnlZ/cl1bC7AAYRpgDU3T5f3bzIYrPDPJx6vq7swC\n9B9l9oKXly5wqF/KbMaPD1TVl6b9d2U25nhPvvbClM1sS5IfyWx89J1Vddf0Z/FXSX5uqvMfu/uj\nS/b7ven7N6vqy1W1d/os8ucGsKaEaYA11t1XZ/bmxHdmNiXew5N8KrNXhH9nZj3Xq9mT5Kwkl2U2\ndOSoJHuT/GaSs7r7QNP1bRavyex/LN6e5P9l1lt/TJLbklyR5Bnd/RvL7PfKJL+c5MPTPt8yfQz7\nADac14kDbGJzrxP/3u5+38a2BoCl9EwDAMAgYRoAAAYJ0wAAMGjLRjcAgEOrqv7qIHd5dXe/ek0a\nA3CEE6YBNrHu3jaw2yKvJ5/3jQM/A4CYzQMAAIYZMw0AAIOEaQAAGCRMAwDAIGEaAAAGCdMAADBI\nmAYAgEH/HzRNv3prn6XJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42227a8490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AzdJXAQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f422266cc90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42263db550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AqARjAACoBGMAAKgEYwAAqARjAACoTPABAHBaMmHHHeeMMQAAJBgDAEAlGAMAQCUYAwBA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42224caed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2J1X18cxu4bw9sytj7Epy/tLB3X1Lkl+cDn8syX1VdV+Sv0vyI0l+OIOrSxxh\nnp7kiiS7q+rBqro3s/m7IrPLze2Yni915fT4giT3V9XtVbW7qq5dq6IB5iUwA8zsSrIls9s7359k\nQ5Ldmd32eUt3f3iF17wss3D83swuA9eZraQ+u7tfv/iSDwk3ZraafnVm21QeTPKFSe5J8pYk35/k\nO7t7z9IXdfeNSb4rs2tcfzLJiUmemOQJa1Y5wJxq79fMBwAArDADAMCAwAwAAAMCMwAADGxc7wIA\n2Leq+p0k37QfL/nT7v7uRdUDcDQRmAEOD49L8qX7OR6AVeAqGQAAMGAPMwAADAjMAAAwIDADAMCA\nwAwAAAMCMwAADAjMAAAw8P8AK42t0ym7AXkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4226133550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3LNZ3UFM9qs11l/9o+/m8vc0VR75nO//Z2xP0djr+mT6lzfbwh9vP9FRHMwQu\nIC7XBhyJtdYPzsz/0SZ4fH6bPbjvbxNeX179xGmu9yUz8ynVN1WPqf5Km3+H/2n1uupXqp9Za/3h\nrkW/sk1Yf2Kb4Z+n+o9tBrl4/sw87wQv+/Q2w1J/1fb1jp9YeN7+G3577eMv3p6U9verh1fH2vwx\nc2uboaJf0oeu1nGu+qnq3W1G83tom+3sntWb2lw3+sfWWr+we6G11v+y3VP89dVD2pzUOdVlZ6hu\n4Bw0ew/CBAAAFxeHUgAAQIIxAABUgjEAAFROvgPgAGbm5k5t1MEXrrWefFT1ABwFwRiAgzjWqQ2b\nfa+jKgTgqLgqBQAA5BhjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqOr/BzM8Wm17daEtAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4226526d10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kv5tp6MrPVdVTtrRA4IgkMAPb1fvm+6dU1UlbWMcH5vvTBnU8Mw/NOvSBNdoc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\nGQAABgRmAAAYEJgBAGBAYAYAgIH/HyITP23jlRKiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42222a3b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1MkxLkiRJjQzTkiRJUiPDtCRJktTIMC1JkiQ1MkxLkiRJjQzTkiRJUiPDtCRJktTIMC1J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\nA/wsQJInAnsBNw2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42262d8e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DyG/vY921mcZ+f6Cq3pvpGdi37OMXCYADTpAGDgf3LrBu2yI7qqq3JvnnmXqd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\nEKQBAGCAIA0AAAMEaQAAGPD/A7kglEs5n6BsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4226080c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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mHJ3hCn3/ML7OZ5J8OcPUbw8c61hJL++5Sa7N0KP5l0luGWt/f4bLYf/XFTzX\ngWxDkh/IMJ74M1V1U1XdnOEqg88b27y4uz8yb7+58d6/Pn6+O8fbC9ambGB/IxgD07YjyZYkr88w\njOGQDEMCfj3Jlu6+0/Rg3f17GcbLvjXD9GFHjPu+K8lTu/sZC138o7v/Ocnjk7xjbH+/JPfPxJjc\n7r42w0lYb84wd+8hGUL3W5I8ZJGLRrw1ybOT/HGSqzN8HX/EWN+2JGd094/P2+fMDJePfm+G3sgj\nMgT3KzKE3G/v7iv28Hp3MvYsPyzJeePz3SXDbAu/k6GHfdee9z6o/EaGfxL+MslHM/zTcrck12X4\n/E7t7pcvsN9LMwzVuWLc5/7jzdAKOEhVdy/dCmAJVbUzQ6h4THe/e7bVAMDK6TEGAIAIxgAAkEQw\nBgCAJMOZvACwpKr61xXu8srufuWqFAOwCgRjYCq6e/Osa2DVLXr57AUcsSpVAKwSs1IAAECMMQYA\ngCSCMQAAJBGMAQAgiWAMAABJBGMAAEgiGAMAQJLk/wNRw5bedHtFcAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42222871d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+tLu/PcnZSV5zoAoHAIC1sMgI86lJdnT3Nd19a5JLkmxe1mdzkldNy5cleXxVVXf/fXf/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\nhnXBNl79AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4226321690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42224daf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f422650fe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kd39glYOxMPP8XX5cksdV1f+qqk9U1cH2PHFkmmc7vzHJy6vqtsw+oerVqxmNFTrU/3ev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\nAAAM6/8DWDbTJLHm+h0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42261abf10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4226167590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f422244fed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42227a6b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4222287b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4222500b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42261c2310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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VAACwA445DHf3+5N88RJrAQCAHeXSagAADEsYBgBgWMIwAADDEoYBABiWMAwAwLCEYQAA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\nAvDYJO+tqo9mCsC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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4222500290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4260265c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f422276ded0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42263e5350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42263cdc10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4264968390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f42263d5110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4222206490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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tBgDAsIRhAACGJQwDADAsYRgAgGEJwwAADEsYBgBgWMIwAADDEoYBABiWMAwAwLCEYQAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4222562750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the rest of the distributions\n",
    "for col in df.columns:\n",
    "    if df.dtypes[col] in ('int64','float64'):\n",
    "        plt.figure(figsize=[12,6])\n",
    "        plt.hist(df[col].dropna(), bins=50, normed=True)\n",
    "        plt.xlabel(col,fontsize=24)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply corrections\n",
    "df.loc[df['age']>89, 'age'] = 91.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 - Write to file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.to_csv('aline_data.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 - Create a propensity score using this data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will create the propensity score using R in the R markdown file `aline_propensity_score.Rmd`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 - Close the connection to the database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "con.close()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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